A preliminary nomogram model for predicting relapse of patients with primary membranous nephropathy

Abstract Objective To explore the predictive factors and establish a nomogram model for predicting relapse risk in primary membranous nephropathy (PMN). Methods The clinical, laboratory, pathological and follow-up data of patients with biopsy-proven membranous nephropathy were collected in the Affiliated Hospital of Qingdao University. A total of 400 PMN patients who achieved remission were assigned to the development group (n = 280) and validation group (n = 120) randomly. Cox regression analysis was performed in the development cohort to determine the predictive factors of relapse in PMN patients, a nomogram model was established based on the multivariate Cox regression analysis and validated in the validation group. C-index and calibration plots were used to evaluate the discrimination and calibration performance of the model respectively. Result Hyperuricemia (HR = 2.938, 95% CI 1.875–4.605, p < 0.001), high C-reactive protein (CRP) (HR = 1.147, 95% CI 1.086–1.211, p < 0.001), and treatment with calcineurin inhibitors with or without glucocorticoids (HR = 2.845, 95%CI 1.361–5.946, p = 0.005) were independent risk factors, while complete remission (HR = 0.420, 95%CI 0.270–0.655, p < 0.001) was a protective factor for relapse of PMN according to multivariate Cox regression analysis, then a nomogram model for predicting relapse of PMN was established combining the above indicators. The C-indices of this model were 0.777 (95%CI 0.729–0.825) and 0.778 (95%CI 0.704–0.853) in the development group and validation group respectively. The calibration plots showed that the predicted relapse probabilities of the model were consistent with the actual probabilities at 1, 2 and 3 years, which indicated favorable performance of this model in predicting the relapse probability of PMN. Conclusions Hyperuricemia, remission status, CRP and therapeutic regimen were predictive factors for relapse of PMN. A novel nomogram model with good discrimination and calibration was constructed to predict relapse risk in patients with PMN early.